Comparison of extended and unscented Kalman, particle, and smooth variable structure filters on a bearing-only target tracking problem

Abstract

In this paper, we study a nonlinear bearing-only target tracking problem using four different estimation strategies and compare their performances. This study is based on a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing angle observations. Four nonlinear estimation strategies are used to track the target: the popular extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The SVSF is a predictor-corrector method used for state and parameter estimation. It is a sliding mode estimator, where gain switching is used to ensure that the estimates converge to true state values. An internal model of the system, either linear or nonlinear, is used to predict an a priori state estimate. A corrective term is then applied to calculate the a posteriori state estimate, and the estimation process is repeated iteratively. The performances of these methods applied on a bearing-only target tracking problem are compared in terms of estimation accuracy and filter robustness. © 2009 SPIE.